Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

改进HSR-FCN的服装图像识别分类算法研究

目前网络上的服装图像数量增长迅猛，对于大量服装图像实现智能分类的需求日益增加。将基于区域的全卷积网络（Region-Based Fully Convolutional Networks，R-FCN）引入到服装图像识别中，针对服装图像分类中网络训练时间长、形变服装图像识别率低的问题，提出一种新颖的改进框架HSR-FCN。新框架将R-FCN中的区域建议网络和HyperNet网络相融合，改变图片特征学习方式，使得HSR-FCN可以在更短的训练时间内达到更高的准确率。在模型中引入了空间转换网络，对输入服装图像和特征图进行了空间变换及对齐，加强了对多角度服装和形变服装的特征学习。实验结果表明，改进后的HSR-FCN模型有效地加强了对形变服装图像的学习，且在训练时间更短的情况下，比原来的网络模型R-FCN平均准确率提高了大约3个百分点，达到96.69%。     

基于时频域的卷积神经网络运动想象脑电信号识别方法

针对目前运动想象脑电（EEG）信号识别率较低的问题，考虑到脑电信号蕴含着丰富的时频信息，提出一种基于时频域的卷积神经网络（CNN）运动想象脑电信号识别方法。首先，利用短时傅里叶变换（STFT）对脑电信号的相关频带进行预处理，并将多个电极的时频图组合构造出一种二维时频图；然后，针对二维时频图的时频特性，通过一维卷积的方法设计了一种新颖的CNN结构；最后，通过支持向量机（SVM）对CNN提取的特征进行分类。基于BCI数据集的实验结果表明，所提方法的平均识别率为86.5%，优于其他传统运动想象脑电信号识别方法；同时将该方法应用在智能轮椅上，验证了其有效性。     

基于多尺度双线性卷积神经网络的多角度下车型精细识别

针对多角度下车辆出现一定的尺度变化和形变导致很难被准确识别的问题，提出基于多尺度双线性卷积神经网络（MS-B-CNN）的车型精细识别模型。首先，对双线性卷积神经网络（B-CNN）算法进行改进，提出MS-B-CNN算法对不同卷积层的特征进行了多尺度融合，以提高特征表达能力；此外，还采用基于中心损失函数与Softmax损失函数联合学习的策略，在Softmax损失函数基础上分别对训练集每个类别在特征空间维护一个类中心，在训练过程中新增加样本时，网络会约束样本的分类中心距离，以提高多角度情况下的车型识别的能力。实验结果显示，该车型识别模型在CompCars数据集上的正确率达到了93.63%，验证了模型在多角度情况下的准确性和鲁棒性。     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

A novel vision-based method for 3D profile extraction of wire harness in robotized assembly process

Automating stages for deformable objects in the production line, in which assembling a wire harness into a predefined position is a complex task owing to the specialized characteristics of the objects. Besides a few automatized systems proposed in the other studies to implement this task under simplified setup conditions, a significant portion of this process remains to be completed manually in industrial environments. To construct an automatic wire harness assembly system, the development of a method that can automatically detect the wire harness profile in a 3D environment and, consequently, guide robot arms to implement assembly tasks is indispensable. Therefore, this study presents an approach that satisfies this requirement, which not only proposes a deep learning-based system to detect the wire profile, but also improves the accuracy of the detected results through a correction method according to the depth values of contiguous areas. The verification of the approach in a robot system that highlights its usefulness and practicality demonstrates the potential of the proposed method to replace people and consequently, reduce labour costs in factory environments.     

基于YOLOv3的安全帽佩戴检测方法研究

在大型锅炉、钢铁和其他制造业工厂中,正确佩戴安全帽是进入现场的前提。但是在现实场景中,由于各种原因,工人伤亡事故中没有正确佩戴安全帽而导致的事故占比一直较高。为了减少该类事故的发生,对深度学习、人工智能展开了研究,采用了卷积神经网络的方法。首先,对相关数据集标定,将数据集总体分为三种类别,针对标定好的数据,使用迁移学习的训练方式训练YOLOv3网络。接着,使用该参数和模型对监控视频数据检测人形和安全帽的类别和位置。最后,对检测的三种类别计算相关交并比,并以此判断工人是否正确佩戴安全帽。该算法创新性地将YOLOv3网络模型和交并比算法结合,对网络输出的类别和位置信息再细化处理,提高了检出的准确性,降低了误识别率。试验结果表明,该算法可以满足安全帽佩戴检测中的实时性要求,同时能够准确检出未佩戴安全帽的人员并通知有关人员。     

基于深度残差网络的自动调制识别方法研究

自动调制识别是电磁环境特性分析的关键问题,而传统方法多基于人工设计特征进行识别,数据特征表示和判别分析能力有限。为此提出一种新颖的深度神经网络特征表示方法进行调制识别任务。首先,利用递归神经网络结构对电磁信号序列进行表示,建立了基于多层双向GRU网络结构的识别方法。其次,从一维空间卷积表示序列的角度思考,建立了基于深度残差卷积网络的调制识别方法。最后,针对加性高斯白噪声信道的调制方式仿真数据集,将提出的方法与典型神经网络模型如多层感知器、卷积神经网络进行了对比实验。实验结果表明,提出的方法在自动调制识别方面具备更强的特征表示能力和竞争力,有利于推动深度学习在自动调制识别领域的应用。     

基于深度学习的管道纱线及其颜色检测

为了保证自动换筒系统中的纱线自动打结机能够正常运行, 需要对管道吸取的纱线进行检测. 纱线纤细、种类繁多且颜色各异, 传感器方法难以胜任, 使用图像处理的方式较为合适. 但是对于纱线检测问题传统的图像处理方法复杂且检测准确率低, 难以解决纱线种类多、尺寸不一以及颜色多等问题, 故本文提出了一种基于Inception v4中Inception-ResNet-A块进行改进的多尺度深度可分离卷积块组成的网络来检测管道中的纱线. 其中改进的多尺度深度可分离卷积块采用3×3卷积核的深度可分离卷积层代替Inception-ResNet-A块中3×3传统卷积层并去除了其中的一些1×1卷积层, 简化卷积块的计算量以及参数量, 此外还结合了残差网络ResNet的方法进行通道融合,防止特征丢失. 试验结果表明, 该网络模型具有非常好的泛化能力以及辨识效果.     

基于卷积神经网络的标识牌识别技术

目前而言,我国标识识别技术正处于飞速发展阶段,具体体现在处理精度、再现性、灵活性、适用面、信息压缩等方面,但是,在实际发展过程中,该技术的发展还是受到了实际需求的限制.深度学习模型运算量大,难以在轻量级嵌入式设备上运行,工业生产中噪声种类繁多复杂,影响识别准确性.针对上述问题,本文提出一种基于卷积神经网络的标识识别技术,利用改进的Canny边缘检测算法,来增强对边缘信息提取时的鲁棒性,实现在高噪声环境下对标识牌精准提取.另外为了进一步提高识别准确率,本文利用CNN和椭圆拟合相结合的思路,把模型识别结果和椭圆拟合结果相结合来判别识别的准确性,在增加少量运算量的同时提高识别准确率.